NLPatent is an industry leading AI-based patent search and analytics platform trusted by Fortune 500 companies, Am Law 100 firms, and research universities around the world. The platform takes an AI-first approach to patent search; it's built from a proprietary Large Language Model trained on patent data to truly understand the language of patents and innovation.
PQAI stands for Patent Quality Artificial Intelligence. It is a free, open-source, natural language-based patent search platform developed by AT&T and the Georgia Intellectual Property Alliance. PQAI is designed as a collaborative initiative to build a shared AI-based tool for prior art searching.
Solve Intelligence is an AI-powered platform designed for intellectual property legal professionals, specializing in streamlining the patenting process. Founded in 2023 and based in San Francisco, the company develops AI tools specifically for patent attorneys, focusing on user-centric design and practical application.
Amplified AI is an intellectual property (IP) technology company offering AI-powered search and collaboration tools. It helps researchers and innovators research, document, and share technical intelligence within their teams by organizing and curating global patent and scientific information.
Ambercite AI is a patent search tool that utilizes artificial intelligence (AI) and network analytics to identify patents similar to a given set of starting patents. It differs from traditional patent searching methods that rely on keywords and patent class codes by using citation patterns, patent text, and metadata to find relevant patents and reduce false positives.
PatentPal is an AI-powered platform designed to streamline the patent drafting process for legal professionals. It utilizes generative AI to automate the creation of patent applications, including generating descriptions, figures, and supporting documents from a set of claims. PatentPal aims to save time for patent attorneys and agents, allowing them to focus on higher-value aspects of their work. It can export drafts into formats like Word, Visio, or PowerPoint.
AP News reports on UK High Court Justice Victoria Sharp's warning that lawyers citing AI-generated fake cases pose 'serious implications for the administration of justice and public confidence in the justice system.' The case highlights growing global judicial concerns about AI misuse in court proceedings, with judges threatening prosecution for attorneys who fail to verify AI-generated research accuracy. This breaking news story exemplifies the urgent need for regulatory frameworks and professional standards governing AI use in legal practice as courts worldwide grapple with maintaining integrity in an AI-enhanced justice system.
ABA's comprehensive legal analysis covers the busiest year in AI legal history, examining copyright battles between algorithmic infringement allegations and fair use defenses while tracking bias, transparency, and privacy litigation trends. The report details landmark cases including USA v. Michel, where criminal convictions involved experimental GenAI program usage, and emphasizes how trial courts are creating de facto AI legal rules absent comprehensive congressional regulation. This authoritative judicial overview demonstrates that judges and bar regulators are increasingly focused on ethical GenAI use rules as litigation shapes AI law development through case-by-case precedent.
Copyright Alliance's comprehensive litigation review tracks over thirty GAI copyright lawsuits including landmark cases like Andersen v. Stability AI and Kadrey v. Meta, with key developments including DMCA dismissals and fair use arguments by defendants. The analysis highlights critical 2024 court rulings that hint at judicial leanings while noting the consolidation of similar cases and the high-stakes nature of these disputes for both creators and AI developers. This detailed case tracking demonstrates how copyright litigation will be pivotal in shaping GAI's future, with courts beginning to address fundamental questions about training data use and fair use defenses.
ABA Journal's technology analysis reveals that 2024 marked a year of contradictions, with rapid AI integration into legal technology that remained largely surface-level despite unprecedented software update rates from vendors. The assessment details how AI transformed deposition analysis, brief drafting, pretrial discovery, and law practice management while noting that usage stabilized after initial rapid adoption. This practitioner-focused review emphasizes the ongoing challenges of leveraging accessible data for analytics and high costs of mainstream AI models, while highlighting AI's growing impact on litigation strategy and case management efficiency.
GWU Law's comprehensive litigation database tracks ongoing and completed AI cases from complaint forward, covering everything from algorithmic bias in hiring and criminal sentencing to autonomous vehicle liability and AI authorship disputes. This unique academic resource provides broad coverage of AI legal disputes including statistical analysis and data protection cases relevant to AI projects, serving as a critical research tool for understanding litigation trends. The database demonstrates the rapidly expanding scope of AI-related legal challenges across multiple domains and its systematic documentation reveals patterns in how courts are addressing novel AI legal questions.
White & Case's regulatory tracker reveals that over 40 state AI bills were introduced in 2023, with Connecticut and Texas enacting AI discrimination assessment statutes, while federal agencies apply existing authorities like the FTC's Rite Aid facial recognition settlement. The analysis highlights how comprehensive state privacy laws like California's CPPA and Illinois's biometric privacy act create overlapping AI compliance requirements, demonstrating the complex regulatory patchwork facing businesses. This authoritative legal tracking emphasizes the practical enforcement reality that existing civil rights, privacy, and consumer protection laws fully apply to AI deployment despite the absence of comprehensive federal AI legislation.